Faculty Bibliography

Local spatial arrangement of nuclei in histopathology images of different cancer subtypes has been shown to have prognostic value. In order to capture localized nuclear architectural information, local cell cluster graph-based measurements have been proposed. However, conventional ways of cell graph construction only utilize nuclear spatial proximity, and do not differentiate between different cell types while constructing the graph. In this paper, we present feature-driven local cell cluster graph (FLocK), a new approach to constructing local cell graphs by simultaneously considering spatial proximity and attributes of the individual nuclei (e.g. shape, size, texture). In addition, we have designed a new set of quantitative graph-derived metrics to be extracted from FLocKs, in turn capturing the interplay between different proximally located clusters of nuclei. We have evaluated the efficacy of FLocK features extracted from H&E stained tissue images in two clinical applications: to classify short-term vs. long-term survival among patients of early stage non-small cell lung cancer (ES-NSCLC), and also to predict human papillomavirus (HPV) status of oropharyngeal squamous cell carcinoma (OP-SCCs). In the classification of long-term vs. short-term survival among patients of ES-NSCLC (training cohort, n = 434), the top 10 discriminative FLocK features related to the variation of FLocK size and intersected FLocK distance were identified, via Minimum Redundancy and Maximum Relevance (MRMR) selection, in 100 runs of 10-fold cross-validation, and in conjunction with a linear discriminant classifier yielded a mean AUC of 0.68 for predicting survival in the training cohort. This is better than other state-of-art histomorphometric and deep learning classifiers (cell cluster graphs (AUC = 0.62), global cell graph (AUC = 0.56), nuclear shape (AUC = 0.54), nuclear orientation (AUC = 0.61), AlexNet (AUC = 0.55), ResNet (AUC = 0.56)). The FLocK-based classifier yielded an AUC of 0.70 in an independent testing cohort (n = 150). The patients identified as "high-risk" had significantly poorer overall survival in the testing cohort, with a hazard ratio (95% confidence interval) of 2.24 (1.24-4.05), p = 0.01144). In the classification of HPV status of OP-SCC, the top three FLocK features pertaining to the portion of intersected FLocKs were used to construct a classifier, which yielded an AUC of 0.80 in the training cohort (n = 50), and an accuracy of 0.78 in an independent testing cohort (n = 35). The combination of FLocK measurements with cell cluster graphs, nuclear orientation, and nuclear shape improved the training AUC to 0.87, 0.91 and 0.85, respectively. Deep learning approaches yielded marginally better performance than the FLocK-based classifier in this application, with AUC = 0.78 for AlexNet, AUC = 0.81 for ResNet, and AUC = 0.76 for FLocK-based classifier in the testing cohort. However, the combination of two hand-crafted features: FLocK and nuclear orientation yielded a better performance (AUC = 0.84). FLocK provides a unique and quantitative way to analyze histology images of solid tumors and interrogate tumor morphology from a different aspect than existing histomorphometrics. The source code can be accessed at https://github.com/hacylu/FLocK.

Background/UNASSIGNED:Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of non-small cell lung carcinomas (NSCLCs). Methods/UNASSIGNED:In this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis. Findings/UNASSIGNED:For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1·48 [95% CI 1·06-2·08]; p=0·022 for The Cancer Genome Atlas [TCGA] LUSC group, 2·24 [1·04-4·80]; p=0·039 for the University of Bern LUSC group, and 1·62 [1·15-2·30]; p=0·0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways. Interpretation/UNASSIGNED:CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy. Funding/UNASSIGNED:National Institue of Health and US Department of Defense.

PURPOSE/OBJECTIVE:Hyperprogression is an atypical response pattern to immune checkpoint inhibition that has been described within non-small cell lung cancer (NSCLC). The paradoxical acceleration of tumor growth after immunotherapy has been associated with significantly shortened survival, and currently, there are no clinically validated biomarkers to identify patients at risk of hyperprogression. EXPERIMENTAL DESIGN/METHODS:=79) with the essential caveat that HPs were evenly distributed among the two sets. A total of 198 radiomic textural patterns from within and around the target nodules and features relating to tortuosity of the nodule associated vasculature were extracted from the pretreatment CT scans. RESULTS:: HR=2.66, 95% CI 1.27 to 5.55; p=0.009). CONCLUSIONS:Our study suggests that image-based radiomics markers extracted from baseline CTs of advanced NSCLC treated with PD-1/PD-L1 inhibitors may help identify patients at risk of hyperprogressions.

BACKGROUND:Immune checkpoint inhibitors have been rapidly adopted for therapy of advanced non-small-cell lung cancer (aNSCLC) based on clinical trial findings. Our aim was to examine outcomes in United States oncology practice settings for patients prescribed pembrolizumab monotherapy for previously treated, programmed death ligand-1 (PD-L1)-expressing aNSCLC, thus clinically similar to patients in the KEYNOTE-010 trial. PATIENTS AND METHODS/METHODS:This retrospective observational study used a nationally representative database to identify adult patients with histologically confirmed aNSCLC and PD-L1 tumor proportion score (TPS) ≥ 1% previously treated with platinum-containing chemotherapy (and appropriate tyrosine kinase inhibitor if nonsquamous aNSCLC with EGFR/ALK genomic tumor aberration). Eligible patients initiated pembrolizumab monotherapy from January 1, 2016, to November 29, 2018; data cutoff was May 31, 2019. The Kaplan-Meier method was used to estimate real-world time on treatment (rwToT) and overall survival (OS). RESULTS:The 349 eligible patients included 199 (57%) men; the median age was 68 years (range, 37-84 years); 70 (25%) of 278 patients with known performance status had Eastern Cooperative Oncology Group score ≥ 2. The median patient follow-up was 8.1 months (range, 1 day to 39.2 months). The median rwToT was 4.9 months (95% confidence interval [CI], 3.7-5.8 months) overall and 5.8 months (95% CI, 4.2-6.6 months) for the TPS ≥ 50% cohort (n = 218). The median OS was 13.8 months (95% CI, 11.0-16.5 months) and 16.5 months (95% CI, 13.7-22.0 months) overall and for TPS ≥ 50%, respectively; 12-month survival rates were 54% and 60%, respectively. CONCLUSION/CONCLUSIONS:Patients treated at oncology practices with pembrolizumab monotherapy for previously treated PD-L1-expressing aNSCLC experienced rwToT and OS similar to treatment duration and OS in phase III clinical trial settings.

BACKGROUND:fusion-positive NSCLC, the efficacy and safety of selective RET inhibition are unknown. METHODS:fusion-positive NSCLC who had previously received platinum-based chemotherapy and those who were previously untreated separately in a phase 1-2 trial of selpercatinib. The primary end point was an objective response (a complete or partial response) as determined by an independent review committee. Secondary end points included the duration of response, progression-free survival, and safety. RESULTS:fusion-positive NSCLC who had previously received at least platinum-based chemotherapy, the percentage with an objective response was 64% (95% confidence interval [CI], 54 to 73). The median duration of response was 17.5 months (95% CI, 12.0 to could not be evaluated), and 63% of the responses were ongoing at a median follow-up of 12.1 months. Among 39 previously untreated patients, the percentage with an objective response was 85% (95% CI, 70 to 94), and 90% of the responses were ongoing at 6 months. Among 11 patients with measurable central nervous system metastasis at enrollment, the percentage with an objective intracranial response was 91% (95% CI, 59 to 100). The most common adverse events of grade 3 or higher were hypertension (in 14% of the patients), an increased alanine aminotransferase level (in 12%), an increased aspartate aminotransferase level (in 10%), hyponatremia (in 6%), and lymphopenia (in 6%). A total of 12 of 531 patients (2%) discontinued selpercatinib because of a drug-related adverse event. CONCLUSIONS:fusion-positive NSCLC who had previously received platinum-based chemotherapy and those who were previously untreated. (Funded by Loxo Oncology and others; LIBRETTO-001 ClinicalTrials.gov number, NCT03157128.).

Background/UNASSIGNED:Lung cancer (LC) development is a process that depends on genetic mutations. The DNA methylation, an important epigenetic modification, is associated with the expression of non-coding RNAs, such as microRNAs. MicroRNAs are particularly essential for cell physiology, since they play a critical role in tumor suppressor gene activity. Furthermore, epigenetic disruptions are the primary event in cell modification, being related to tumorigenesis. In this context, microRNAs can be a useful tool in the LC suppression, consequently improving prognosis and predicting treatment. Conclusion/UNASSIGNED:This manuscript reviews the main microRNAs involved in LC and its potential clinical applications to improve outcomes, such as survival and better quality of life.

BACKGROUND:Use of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery. METHODS:. FINDINGS:). INTERPRETATION:QuRiS and QuRNom were validated as being prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy, especially in clinically defined low-risk groups. Since QuRiS is based on routine chest CT imaging, with additional multisite independent validation it could potentially be employed for decision management in non-invasive treatment of resectable lung cancer. FUNDING:National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defence, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation.

Summary/: Background:Development and validation of a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (ES-NSCLC) that is prognostic of disease-free survival (DFS) and predictive of the added benefit of adjuvant chemotherapy (ACT) following surgery. Methods:. Findings:,p<0·05, N=86) and other immune specific biological pathways. Interpretation:QuRiS and QuRNom were validated as being prognostic of DFS and predictive of the added benefit of ACT.

OBJECTIVES/OBJECTIVE:To evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT). MATERIALS AND METHODS/METHODS:) validation sets. A linear discriminant analysis (LDA) classifier was built based on the most stable and discriminate features. In addition, a radiomic risk score (RRS) was generated by using least absolute shrinkage and selection operator, Cox regression model to predict time to progression (TTP) following surgery. RESULTS:, 0.76 vs. 0.63). The RRS generated by most stable-discriminating features was significantly associated with TTP compared to discriminating alone criteria (HR = 1.66, C-index of 0.72 vs. HR = 1.04, C-index of 0.62). CONCLUSION/CONCLUSIONS:Accounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.